Information processing device, image processing method and medium

ABSTRACT

An information processing device according to the present invention includes: a proper identifier output unit which outputs proper identifiers for identifying learning images; a feature vector calculation unit which calculates feature vectors of at least a part of patches included in registered patches that are registered in a dictionary for compositing a restored image; and a search similarity calculation unit which calculates a similarity calculation method that classifies the proper identifiers to be given to the registered patches based on the feature vectors.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a national stage of International Application No.PCT/JP2014/001704 filed Mar. 25, 2014, claiming priority based onJapanese Patent Application No. 2013-079338 filed Apr. 5, 2013, theentire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to an image information processingtechnology, and more particularly to restoration processing of blurredimages.

BACKGROUND ART

Image processing technologies to process digital image data have beenwidely known. As one of those image processing technologies, a blurrestoration technology has been known. The blur restoration technologyis a technology to restore blurred images.

The blur restoration technologies include, for example, a noise removaltechnology (denoise), a haze removal technology (dehaze), and a superresolution technology (super-resolution) (for example, refer to NPL 1).The super resolution technology will be described below as an example ofthe blur restoration technologies.

The super resolution technology is an image processing technology toraise the resolution of image data. The super resolution technologiesinclude, for example, the following two technologies.

The first super resolution technology is a multiple-frame superresolution technology. The multiple-frame super resolution technology isa technology to generate a piece of high resolution image data by usinga plurality of pieces of image data (a plurality of frames) thatcomposes a motion video or are generated by consecutive shooting (forexample, refer to PLT 1). As described above, the multiple-frame superresolution technology requires a plurality of pieces of image data toachieve high resolution. Thus, the multiple-frame super resolutiontechnology is incapable of generating a piece of high resolution imagedata from a piece of image data.

The second super resolution technology is a learning based superresolution technology. The learning based super resolution technology isa technology to create a dictionary based on learning processing inadvance and raise the resolution of a piece of image data by using thedictionary (for example, refer to PLTs 2 and 3). Since the learningbased super resolution technology uses a dictionary, the learning basedsuper resolution technology is capable of achieving a higher superresolution than the multiple-frame super resolution technology that usesa smaller number of pieces of referenced image data.

The learning based super resolution technology will be further describedwith reference to the drawings. The learning based super resolutiontechnology includes “a learning phase” and “a super resolution phase” ingeneral. “The learning phase” is a phase in which a dictionary that isused for super resolution processing is created. “The super resolutionphase” is a phase in which a high resolution image is generated from alow resolution image by using the dictionary.

In the learning based super resolution technology, a device may carryout both phases. Alternatively, a plurality of devices may carry out therespective phases individually.

To make the description clearer, description using devices for therespective phases will be made below.

FIG. 10 is a diagram illustrating an example of a configuration of asuper resolution system 900 that is related to the present invention.

The super resolution system 900 includes a dictionary creation device910, a dictionary 920, and a super resolution image generation device930.

The dictionary creation device 910 carries out a learning phase.Specifically, the dictionary creation device 910 creates patches (patchpairs 531), which are used in a super resolution phase, based onlearning images 51, and stores the created patch pairs 531 in thedictionary 920.

The dictionary 920 stores the patch pairs 531 which the dictionarycreation device 910 creates for the creation of a super resolutionimage.

The super resolution image generation device 930 carries out the superresolution phase. Specifically, the super resolution image generationdevice 930 generates a restored image 55 (a high resolution image) byusing an input image 54 (a low resolution image) and the patch pairs531, which are stored in the dictionary 920.

The respective phases will be further described.

FIG. 11 is a diagram for a description of the learning phase. Processingin the learning phase will be described by using FIGS. 10 and 11 incombination.

The dictionary creation device 910 receives high resolution images forlearning (the learning images 51). The dictionary creation device 910generates low resolution images (blurred images 52) by lowering theresolution of the learning images 51.

The dictionary creation device 910 cuts out image portions withinpredetermined ranges (high resolution patches 511) from the learningimages 51. Further, the dictionary creation device 910 cuts out imageportions (low resolution patches 521), that correspond to the cut-outhigh resolution patches 511, from the blurred images 52.

The dictionary creation device 910 generates patch pairs 531 bycombining the high resolution patches 511 with the low resolutionpatches 521. The dictionary creation device 910 stores the patch pairs531 in the dictionary 920.

FIG. 12 is a diagram for a description of the super resolution phase.

The super resolution image generation device 930 receives the inputimage 54.

Based on the input image 54, the super resolution image generationdevice 930 generates patches (input patches 541) to be compared with thelow resolution patches 521 in the patch pairs 531.

Based on the generated input patches 541, the super resolution imagegeneration device 930 selects patch pairs 531 by referring to thedictionary 920. More specifically, the super resolution image generationdevice 930 operates, for example, in the following manner.

The super resolution image generation device 930 calculates similaritiesbetween the input patch 541 and the low resolution patches 521 in allpatch pairs 531. Based on the similarities, the super resolution imagegeneration device 930 selects a patch pair 531 that includes the mostsimilar low resolution patch 521. The high resolution patch 511 of theselected patch pair 531 becomes a patch (a restoration patch 551) thatis used for compositing.

The super resolution image generation device 930 selects patch pairs 531that correspond to all input patches 541. By using high resolutionpatches 511 in the selected patch pairs 531 as restoration patches 551,the super resolution image generation device 930 generates a restoredimage 55 (a super resolution image).

CITATION LIST Patent Literature

[PLT 1] Japanese Unexamined Patent Application Publication No.2009-181508

[PLT 2] Japanese Unexamined Patent Application Publication No.2011-170456

[PLT 3] Japanese Unexamined Patent Application Publication No.2012-043437

Non Patent Literature

[NPL 1] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm forimage denoising”, IEEE Computer Society Conference on Computer Visionand Pattern Recognition 2005 (CVPR2005), Volume: 2, Page(s) 60-65, Jun.20-25, 2005.

SUMMARY OF INVENTION Technical Problem

The learning images 51 include a lot of types of images. Therefore, thelearning images 51 include the same type of images as the input image 54and different types of images from the input image 54. In other words,the patch pairs 531 that the dictionary 920 holds are created from a lotof types of learning images 51.

The super resolution technologies disclosed in the above-described PLTs1 to 3, which are related to the present invention, compare similaritiesbetween the input patch 541 and all patch pairs 531 in the superresolution phase without discriminating patch pairs 531. Thus, there isa case in which the super resolution technologies disclosed in PLTs 1 to3 select a patch pair 531 of an image of a different type from the inputimage 54.

As described above, there has been a problem in that the superresolution technologies disclosed in PLTs 1 to 3 are incapable ofselecting an appropriate patch pair 531.

Since a technology disclosed in NPL 1 does not use a dictionary, thetechnology is incapable of dealing with the above-described problem.

An object of the present invention is to solve the above-describedproblem and provide an information processing device and an imageprocessing method that make it possible to restore blurred imagesappropriately.

Solution to Problem

An information processing device according to an aspect of the presentinvention, includes: a proper identifier output unit which outputsproper identifiers for identifying learning images; a feature vectorcalculation unit which calculates feature vectors of at least a part ofpatches included in registered patches that are registered in adictionary for compositing a restored image; and a search similaritycalculation unit which calculates a similarity calculation method thatclassifies the proper identifiers to be given to the registered patchesbased on the feature vectors.

An image processing method according an aspect of the present invention,includes: outputting proper identifiers for identifying learning images;calculating feature vectors of at least a part of patches included inregistered patches that are registered in a dictionary for compositing arestored image; and calculating a similarity calculation method thatclassifies the proper identifiers to be given to the registered patchesbased on the feature vectors.

A computer-readable recording medium according to an aspect of thepresent invention, the medium embodying a program, the program causing acomputer device to perform a method, the method comprising: outputtingproper identifiers for identifying learning images; calculating featurevectors of at least a part of patches included in registered patchesthat are registered in a dictionary for compositing a restored image;and calculating a similarity calculation method that classifies theproper identifiers to be given to the registered patches based on thefeature vectors.

Advantageous Effects of Invention

With the present invention, it is possible to restore blurred imagesappropriately.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example of a configuration ofa super resolution system that includes an information processing deviceaccording to a first exemplary embodiment of the present invention;

FIG. 2 is a diagram illustrating an example of data that the informationprocessing device of the first exemplary embodiment registers in adictionary;

FIG. 3 is a diagram for a description of classification in the firstexemplary embodiment;

FIG. 4 is a flowchart illustrating an example of an operation of thefirst exemplary embodiment;

FIG. 5 is a block diagram illustrating an example of a variation of theinformation processing device of the first exemplary embodiment;

FIG. 6 is a block diagram illustrating an example of a variation of theinformation processing device of the first exemplary embodiment;

FIG. 7 is a block diagram illustrating an example of a configuration ofan information processing device of a second exemplary embodiment of thepresent invention;

FIG. 8 is a block diagram illustrating an example of a configuration ofan information processing device of a third exemplary embodiment of thepresent invention;

FIG. 9 is a block diagram illustrating an example of a configuration ofan information processing device of a fourth exemplary embodiment of thepresent invention;

FIG. 10 is a block diagram for a description of an example of aconfiguration of a super resolution system that is related to thepresent invention;

FIG. 11 is a diagram for a description of a learning phase that isrelated to the present invention; and

FIG. 12 is a diagram for a description of a super resolution phase thatis related to the present invention.

DESCRIPTION OF EMBODIMENTS

Next, exemplary embodiments of the present invention will be describedwith reference to the drawings.

The respective drawings are for a description of the exemplaryembodiments of the present invention. Thus, the present invention is notlimited to the illustrations in the respective drawings.

The same components in the respective drawings will be denoted by thesame reference numerals and a repetitive description thereof may beomitted.

(First Exemplary Embodiment)

First, an information processing device 10 of a first exemplaryembodiment, which is an exemplary embodiment of the present invention,will be described.

There is no limitation to a blur restoration technology that is appliedto the information processing device 10 of the exemplary embodiment.Thus, the following description will be made by using a super resolutiontechnology as an illustrative example.

FIG. 1 is a block diagram illustrating an example of a configuration ofa super resolution system 40 that includes the information processingdevice 10 according to the first exemplary embodiment of the presentinvention.

The super resolution system 40 includes the information processingdevice 10, a dictionary 20, and a super resolution image generationdevice 30.

The information processing device 10 in the exemplary embodiment carriesout a learning phase in super resolution processing.

For that purpose, the information processing device 10 includes alearning-use high resolution image reception unit 110, a blurred imagegeneration unit 120, a proper ID (Identifier) output unit 130, a patchgeneration unit 140, a feature vector calculation unit 150, a searchsimilarity calculation unit 160, and a registration unit 170.

FIG. 2 is a diagram illustrating an example of data that the informationprocessing device 10 of the exemplary embodiment registers (stores) inthe dictionary 20.

The description below will also be made with reference to FIG. 2. InFIG. 2, the same reference numerals are assigned to the same componentsas in FIG. 11. In the following description, “registering (storing) datain the dictionary 20” is referred to as “creating the dictionary 20”.

The learning-use high resolution image reception unit 110 receives highresolution images for use in learning (learning images 51 illustrated inFIG. 2) to create the dictionary 20. There is no particular limitationto a reception method of the learning images 51. For example, anoperator of the super resolution system 40 may input the learning images51 to the information processing device 10. The information processingdevice 10 may receive information that specifies a storage place of thelearning images 51 from a not-illustrated input device of the superresolution system 40, and receive the learning images 51 from thespecified storage place.

The blurred image generation unit 120 generates low resolution images(blurred images 52 illustrated in FIG. 2), which are generated bylowering the resolutions of the learning images 51. The blurred imagegeneration unit 120 may generate a plurality of blurred images 52 byusing a plurality of blurring methods.

The proper ID output unit 130 outputs identifiers (hereinafter, referredto as “proper identifiers” or “proper IDs (Identifiers)”) that areassigned to registered patches 200, which the information processingdevice 10 registers in the dictionary 20 as will be described later. Inother words, the proper ID output unit 130 is a proper identifier outputunit. The proper ID output unit 130 may acquire proper IDs based on somemethod and output the acquired proper IDs, as will be described later.In other words, the proper ID output unit 130 may also be a properidentifier acquisition unit.

The following description will be made under the assumption that “theproper ID” is an identifier that is assigned to a learning image 51.However, the proper ID does not have to be an ID that is assigned toeach learning image 51. The proper ID output unit 130 may, for example,acquire, as a proper ID, an ID that is assigned to each predeterminedregion in the learning image 51 and output the acquired ID.Alternatively, the proper ID output unit 130 may acquire, as a properID, an ID that is assigned to each registered patch 200 and output theacquired ID.

There is no particular limitation to the value and data form of a properID. For example, in the cases of a portrait image and a facial image,the proper ID may be an identifier (ID) of a person in the image.

Alternatively, the proper ID may be assigned in accordance with the typeof an image. For example, the proper ID may be an identifier by whichthe type of a portion of a portrait image (for example, a facial imageor an upper body image) can be classified. The proper ID may also be anidentifier that enables to classify within the same type with respect toeach type of a portion of a portrait image.

When images include predetermined numerical values (for example, alicense plate image), the proper IDs may be numbers included in theimages.

There is no particular limitation to the acquisition method of theproper ID. For example, the information processing device 10 may acquireproper IDs corresponding to the learning images 51 based on an inputoperation carried out by an operator. Alternatively, the proper IDoutput unit 130 may assign proper IDs to the learning images 51sequentially.

The patch generation unit 140 generates (extracts) patches (for example,high resolution patches 511 and low resolution patches 521 in FIG. 2)that are included in registered patches 200 to be registered in thedictionary 20, based on the learning images 51 and blurred images 52that the blurred image generation unit 120 has generated.

The dictionary 20 has no limitation to the storage method of patches.The information processing device 10 stores not only the high resolutionpatches 511 and the low resolution patches 521 but also otherinformation as information that is stored in the dictionary 20, as willbe described later. Thus, a set of information that is stored in thedictionary 20, including a plurality of patches (for example, a patchpair) and information other than the patches, is referred to as“registered patch 200” in the following description.

Further, the patch generation unit 140 assigns “proper IDs”, which arereceived from the proper ID output unit 130, to the registered patches200 including generated patches. As described above, the proper ID thatis used in the description of the exemplary embodiment is an identifierthat is assigned to each learning image 51. In other words, the properID output unit 130 assigns “the proper ID” of a learning image 51 toregistered patches 200.

The feature vector calculation unit 150 receives the low resolutionpatches 521, which are included in the registered patches 200, from thepatch generation unit 140. Further, the feature vector calculation unit150 calculates “feature vectors” of the low resolution patches 521,which are included in the registered patches 200. In other words, thefeature vector calculation unit 150 calculates feature vectors by usingat least a part of patches among the registered patches 200.

The feature vector is a vector that is used in a search in a superresolution phase. Thus, it is preferable that the feature vectorcalculation unit 150 calculates the feature vectors in line with thesuper resolution phase.

For example, the feature vector calculation unit 150 may use “abrightness vector”, which is used in a general super resolution phase,as a feature vector. Alternatively, the feature vector calculation unit150 may use “a BRIEF (Binary Robust Independent Elementary Feature)feature”, which is a binary feature.

The feature vector calculation unit 150 may, in substitution for thepatch generation unit 140, receive proper IDs from the proper ID outputunit 130 and assign the proper IDs to the registered patches 200. Thedashed line illustrated in FIG. 1 illustrates a dataflow connection fromthe proper ID output unit 130 to the feature vector calculation unit 150in this case.

The search similarity calculation unit 160 calculates “a similaritycalculation method” that classifies the proper IDs assigned to theregistered patches 200, based on the feature vectors that the featurevector calculation unit 150 calculates.

“The similarity calculation method” means a method by which the superresolution image generation device 30 calculates similarities betweenpatches of an input image 54 and the registered patches 200, as will bedescribed later. To calculate “the similarity calculation method” meansto select “the similarity calculation method” that is used forclassifying of proper IDs based on values that are calculated byapplying “the similarity calculation methods” to the proper IDs.

The calculation of “the similarity calculation method” in the searchsimilarity calculation unit 160 is, for example, an operation that willbe described below.

First, the search similarity calculation unit 160 applies featurevectors to methods to calculate similarities of feature vectors. Thesearch similarity calculation unit 160 classifies the proper IDs basedon results of the calculation. The search similarity calculation unit160 calculates states of classification of the proper IDs. The state ofclassification is, for example, the mean value of distances betweengroups of the classified proper IDs or distances between the proper IDswithin the groups after classifying. The search similarity calculationunit 160 selects the similarity calculation method that classifies theproper IDs most appropriately (for example, a method that produces longdistances between the groups after classifying), based on the calculatedstates of classification.

“The similarity calculation method” is a method by which the superresolution image generation device 30 calculates similarities betweenpatches of the input image 54 and the registered patches 200. Forexample, the similarity calculation method may include a coefficient(s)or a coefficient matrix of a formula that is used in the similaritycalculation. Alternatively, the similarity calculation method mayinclude a decision method of similarity, such as a decision method ofthe difference between patches, or a scale of distance used fordecision.

Further, the similarity calculation method may include a plurality ofitems described above (a coefficient(s), a coefficient matrix, adecision method, or a scale of distance), and may include informationother than the above items, which is required for calculation ofsimilarity.

The search similarity calculation unit 160 may calculate a plurality ofsimilarity calculation methods.

The operation of the search similarity calculation unit 160 will bedescribed in more detail with reference to FIG. 3.

FIG. 3 is a diagram schematically illustrating a distribution of featurevectors of the registered patches 200 in a feature vector space.

In FIG. 3, the squares illustrate the registered patches 200 which areassigned “ID1s” to “the proper IDs”. In a similar manner, the trianglesillustrate the registered patches 200 which are assigned “ID2s” to “theproper IDs”. The circles illustrate the registered patches 200 which areassigned “ID3s” to “the proper IDs”.

The search similarity calculation unit 160 calculates the similaritycalculation method in such a way as to appropriately classify therespective proper IDs in the feature vector space.

For example, the dashed lines illustrated in FIG. 3 indicate an exampleof classifying of the proper IDs. In other words, the search similaritycalculation unit 160 calculates the similarity calculation method thatachieves classification of the proper IDs to be represented by thedashed lines illustrated in FIG. 3.

Specifically, the search similarity calculation unit 160 should, forexample, calculate the similarity calculation method by using thefollowing methods.

(1) Fisher Discriminant

(2) Support Vector Machine (SVM)

(3) Subspace Method

(4) Local Fisher Discriminate Analysis (LFDA)

The search similarity calculation unit 160 may reduce dimensions byusing the following methods before carrying out the above-describedprocessing.

(1) Principal Component Analysis (PCA)

(2) Kernel Principal Component Analysis (KPCA)

(3) Locality Preserving Projection (LPP)

Alternatively, the search similarity calculation unit 160 may useSemi-Supervised LFDA (SELF).

The description returns to the description with reference to FIGS. 1 and2.

The registration unit 170 registers the registered patches 200 eachincluding patches and the like, which will be described below, in thedictionary 20.

(1) “Patches (a high resolution patch 511 and a low resolution patch521)” generated by the patch generation unit 140

(2) “A proper ID” output by the proper ID output unit 130

(3) “A similarity calculation method” calculated by the searchsimilarity calculation unit 160

Next, an operation of the information processing device 10 will bedescribed with reference to the drawings.

FIG. 4 is a flowchart illustrating an example of an operation of theinformation processing device 10.

The learning-use high resolution image reception unit 110 of theinformation processing device 10 receives learning images 51 (stepS400).

Next, the blurred image generation unit 120 generates blurred imagesbased on the learning images 51 (step S401).

The proper ID output unit 130 outputs proper IDs (step S402).

The patch generation unit 140 generates patches (high resolution patches511 and low resolution patches 521) which are included in registeredpatches 200, based on the learning images 51 and the blurred images 52(step S403). The patch generation unit 140 may set the proper IDs to theregistered patches 200.

The feature vector calculation unit 150 calculates feature vectors basedon the low resolution patches 521 included in the registered patches 200(step S404).

The search similarity calculation unit 160 calculates a similaritycalculation method that classifies the proper IDs appropriately, basedon the feature vectors (step S405).

The registration unit 170 registers the registered patches 200, each ofwhich includes “the proper ID” output by the proper ID output unit 130,“the patches” generated by the patch generation unit 140, and “thesimilarity calculation method” calculated by the search similaritycalculation unit 160, in the dictionary 20 (step S406).

In this way, the information processing device 10 of the exemplaryembodiment registers the registered patches 200, each of which includesthe similarity calculation method that is suitable for classification ofthe proper IDs, in the dictionary 20.

Therefore, the super resolution image generation device 30 is able touse the similarity calculation method when the super resolution imagegeneration device 30 uses the dictionary 20 that the informationprocessing device 10 of the exemplary embodiment registers.

To gain a deeper understanding of the information processing device 10of the exemplary embodiment, an example of the super resolution imagegeneration device 30, which uses the dictionary 20 that the informationprocessing device 10 of the exemplary embodiment registers, will bedescribed.

A case in which the super resolution image generation device 30 selectsa registered patch 200 by using a proper ID will be described below asan example. However, the super resolution image generation device 30does not have to use a proper ID to select a registered patch 200.

The super resolution image generation device 30 carries out the superresolution phase.

For that purpose, the super resolution image generation device 30includes, for example, a low resolution image reception unit 310, apatch generation unit 320, a feature vector calculation unit 330, aselection unit 340, and a compositing unit 350.

The low resolution image reception unit 310 receives an input image 54.The low resolution image reception unit 310 acquires the proper ID ofthe input image 54.

The low resolution image reception unit 310 may acquire the proper p IDin any manner. For example, the low resolution image reception unit 310may receive the proper ID together with the input image 54.Alternatively, the low resolution image reception unit 310 may acquirethe proper ID from a predetermined database based on the input image 54.

The patch generation unit 320, based on the input image 54, generatespatches (for example, an input patch 541, which is illustrated in FIG.12) for comparison with the registered patches 200 in the dictionary 20.

The patch generation unit 320 may set the proper ID, which the lowresolution image reception unit 310 acquires, to the generated patches(the input patches 541).

The feature vector calculation unit 330 calculates feature vectors ofthe patches generated by the patch generation unit 320.

The selection unit 340 selects a registered patch 200 that correspondsto each patch of the input image 54 and is included in the dictionary20, based on the feature vector calculated by the feature vectorcalculation unit 330 and the proper ID.

For example, first, the selection unit 340 selects registered patches200 that include the same proper ID as the proper ID acquired by the lowresolution image reception unit 310. Then, the selection unit 340selects a similar registered patch 200 from among the selectedregistered patches 200 based on the feature vector.

As already been described, the selection unit 340 does not have to usethe proper ID for selection of a registered patch 200.

The registered patches 200 of the exemplary embodiment include thesimilarity calculation method that the information processing device 10has calculated.

Thus, by using the similarity calculation method included in theregistered patches 200, the selection unit 340 calculates similaritiesbetween the registered patches 200 and a patch of the input image 54,and selects a registered patch 200 that has a close similarity.

A description using a specific example will be made below.

For example, it is assumed that “i” denotes the number of a registeredpatch 200 and “j” denotes the number of an input patch 541. Further, itis assumed that “A_(i)” denotes the feature vector of the low resolutionpatch 521 included in the “i-th” registered patch 200. It is alsoassumed that “D_(i)(X, Y)” denotes the similarity calculation methodthat is included in the registered patch 200. “D_(i)(X, Y)” is asimilarity calculation method that calculates a similarity betweenvectors X and Y. It is assumed that, the smaller a value calculated by“D_(i)(X, Y)” is, the more similar the registered patch 200 becomes tothe input patch 541. It is also assumed that “B_(j)” denotes the featurevector of the “j-th” input patch 541.

Then, the selection unit 340 calculates “D_(i)(A_(i), B_(j))” as asimilarity. The selection unit 340 calculates similarities in the samemanner with respect to all registered patches 200 within a range ofregistered patches 200 for which similarities are calculated. Theselection unit 340 selects registered patches 200 in a predeterminedrange (for example, a predetermined number of registered patches 200 inascending order of similarities).

The selection unit 340 may select a registered patch 200 or may select,without limited to one registered patch 200, a plurality of registeredpatches 200.

The compositing unit 350 composites a restored image 55 (a superresolution image) by using the high resolution patches 511 of theregistered patches 200 which the selection unit 340 has selected.

As described above, the super resolution image generation device 30 iscapable of selecting the registered patches 200 that are used forcompositing the restored image 55 (a super resolution image) by usingthe similarity calculation method that classifies the proper IDsappropriately.

In other words, the information processing device 10 registers thesimilarity calculation method that classifies the proper IDs of thelearning images 51 appropriately and thereby improves the superresolution processing.

The information processing device 10 may associate a blurring method ofthe blurred image generation unit 120 with a proper ID. In other words,the information processing device 10 may assign a proper ID with respectto each of a plurality of blurring methods which are carried out by theblurred image generation unit 120.

An advantageous effect of the information processing device 10 of theexemplary embodiment will be described.

The information processing device 10 of the exemplary embodiment canachieve an advantageous effect in that the dictionary 20, by which thesuper resolution image generation device 30 is able to select suitableregistered patches 200, is generated.

In other words, the information processing device 10 can achieve anadvantageous effect in that, in the super resolution image generationdevice 30, a blurred image is restored appropriately.

The reason for the advantageous effect is as follows.

The proper ID output unit 130 of the information processing device 10outputs proper IDs that correspond to learning images 51 (learning-usehigh resolution images). The search similarity calculation unit 160 ofthe information processing device 10 calculates a similarity calculationmethod of registered patches 200 in such a way that the proper IDsassigned to the learning images 51 are classified appropriately. Theinformation processing device 10 registers the registered patches 200including the similarity calculation method in the dictionary 20.

In consequence, by using the similarity calculation method included inthe registered patches 200, which the information processing device 10of the exemplary embodiment has registered, in the dictionary 20, thesuper resolution image generation device 30 is capable of selectingregistered patches 200 that are suitable for an input image 54 andcompositing (restoring) a proper restored image 55.

In other words, the information processing device 10 of the exemplaryembodiment is capable of registering, in the dictionary 20, registeredpatches 200 by which the super resolution image generation device 30 isable to restore a composite image from a blurred image appropriately.

<First Variation>

The configuration of the information processing device 10 is not limitedto the configuration described thus far.

The information processing device 10 may have a configuration in whicheach component is divided into a plurality of components.

Further, the information processing device 10 does not have to beconfigured with a device. For example, the information processing device10 may be configured as an information processing system in which adevice to generate patches, which includes the patch generation unit140, and a device to calculate a similarity calculation method, whichincludes the search similarity calculation unit 160, are interconnectedvia a network.

The information processing device 10 may store a similarity calculationmethod, which is calculated based on proper IDs, by including thesimilarity calculation method in the registered patches 200 that havebeen registered in the dictionary 20. Thus, the information processingdevice 10 may store the registered patches 200 in the dictionary 20first and thereafter calculate the similarity calculation method and addthe similarity calculation method to the stored registered patches 200.Alternatively, the information processing device 10 may create or updatethe similarity calculation method of the registered patches 200 thathave been stored in the dictionary 20.

FIG. 5 is a block diagram illustrating an example of a configuration ofan information processing device 11, which is a variation of theinformation processing device 10. In FIG. 5, the same components as inFIG. 1 will be denoted by the same reference numerals and a detaileddescription thereof will be omitted. Although the information processingdevice 11 illustrated in FIG. 5 registers or updates a similaritycalculation method and a proper ID in a registered patch 200, theinformation processing device 11 does not register other informationincluded in the registered patch 200 (for example, a high resolutionpatch 511 and a low resolution patch 521). Thus, in FIG. 5, theregistration unit 170 is omitted.

The information processing device 11 includes the proper ID output unit130, the feature vector calculation unit 150, and the search similaritycalculation unit 160.

The proper ID output unit 130 outputs proper IDs that are assigned tothe registered patches 200 for which a similarity calculation method iscalculated.

The feature vector calculation unit 150 retrieves low resolution patchesfor which feature vectors are calculated from the dictionary 20 andcalculates the feature vectors.

The search similarity calculation unit 160 calculates a similaritycalculation method in such a way that the proper IDs are classifiedappropriately and registers the calculated similarity calculation methodin the registered patches 200 in the dictionary 20. The searchsimilarity calculation unit 160 may register the proper IDs in theregistered patches 200.

The information processing device 11 may receive the registered patches200 from a not-illustrated device.

For example, the feature vector calculation unit 150 receives theregistered patches 200 from a not-illustrated device and calculates thefeature vectors. The proper ID output unit 130 outputs proper IDs. Basedon the proper IDs and the feature vectors, the search similaritycalculation unit 160 calculates a similarity calculation method. Thesearch similarity calculation unit 160 may register, as the registeredpatches 200, the patches that the feature vector calculation unit 150has received, the similarity calculation method, and the proper IDs inthe dictionary 20.

The information processing device 11, which is configured in this way,is able to achieve an advantageous effect that is equivalent to theadvantageous effect that the information processing device 10 achieves.

The reason for the advantageous effect is as follows.

The reason is that the information processing device is, in the samemanner as the information processing device 10, capable of calculating asimilarity calculation method for the registered patches 200 in such away that the proper IDs can be classified appropriately, based on thefeature vectors of the registered patches 200 in the dictionary 20, andregistering the calculated similarity calculation method in thedictionary 20.

<Second Variation>

The information processing device 10 may be configured in such a waythat a plurality of components are combined into a component.

For example, the information processing device 10 may be configured as acomputer device that includes a CPU (Central Processing Unit), a ROM(Read Only Memory), and a RAM (Random Access Memory). In addition to theabove-described configuration, the information processing device 10 maybe configured as a computer device that further includes an Input OutputCircuit (IOC) and a Network Interface Circuit (NIC).

FIG. 6 is a block diagram illustrating an example of a configuration ofan information processing device 60, which is a variation of theinformation processing device 10 of the exemplary embodiment.

The information processing device 60 includes a CPU 610, a ROM 620, aRAM 630, an internal storage device 640, an IOC 650, and an NIC 680, andcomposes a computer device.

The CPU 610 reads a program from the ROM 620. Based on the read program,the CPU 610 controls the RAM 630, the internal storage device 640, theIOC 650, and the NIC 680. The CPU 610 controls these components andthereby achieves functions of respective components illustrated inFIG. 1. The respective components are the learning-use high resolutionimage reception unit 110, the blurred image generation unit 120, theproper ID output unit 130, the patch generation unit 140, the featurevector calculation unit 150, the search similarity calculation unit 160,and the registration unit 170. The CPU 610, in achieving functions ofthe respective components, may use the RAM 630 as a temporary storagefor the program.

The CPU 610 may read a program, which is included in a storage medium700 that stores programs in a computer-readable manner, by using anot-illustrated storage medium reading device, store the program in theRAM 630, and execute the stored program. Alternatively, the CPU 610 mayreceive a program from a not-illustrated external device via the NIC680, store the program in the RAM 630, and execute the stored program.

The ROM 620 stores programs that the CPU 610 executes and static data.The ROM 620 is, for example, a P-ROM (Programmable-ROM) or a flash ROM.

The RAM 630 temporarily stores programs that the CPU 610 executes ordata. The RAM 630 is, for example, a D-RAM (Dynamic-RAM).

The internal storage device 640 stores programs and data that theinformation processing device 60 keeps on a long-term. The internalstorage device 640 may function as a transitory storage device for theCPU 610. Alternatively, the internal storage device 640 may storeprograms that the CPU 610 executes. The internal storage device 640 is,for example, a hard disk device, a magneto optical disk, an SSD (SolidState Drive), or a disk array device.

The IOC 650 mediates data between the CPU 610 and an input device 660and/or a display device 670. The IOC 650 is, for example, an 10interface card or a USB (Universal Serial Bus) card.

The input device 660 is a device that receives instructions input by anoperator of the information processing device 60. The input device 660is, for example, a keyboard, a mouse, or a touch panel.

The display device 670 is a device that displays information to theoperator of the information processing device 60. The display device 670is, for example, a liquid crystal display.

The NIC 680 relays data exchange with an external device via thenetwork. The NIC 680 is, for example, a LAN (Local Area Network) card.

The information processing device 60, which is configured in such amanner, is able to achieve the same advantageous effect as theinformation processing device 10.

The reason for the advantageous effect is as follows.

The reason is that the CPU 610 of the information processing device 60is able to achieve the same functions as the information processingdevice 10 based on programs.

(Second Exemplary Embodiment)

FIG. 7 is a block diagram illustrating an example of a configuration ofan information processing device 12 according to a second exemplaryembodiment.

In FIG. 7, the same reference numerals are assigned to the samecomponents as in FIG. 1. Thus, description of the same configuration andoperations as the first exemplary embodiment will be omitted, andcomponents specific to the exemplary embodiment will be described.

The information processing device 12 may be configured with a computerdevice illustrated in FIG. 6 as with the information processing device10.

The information processing device 12 includes a proper ID output unit132 in substitution for the proper ID output unit 130, which is includedin the information processing device 10 illustrated in FIG. 1.

The proper ID output unit 132 does not acquire values of proper IDsdirectly but calculates proper IDs based on learning images 51 that alearning-use high resolution image reception unit 110 receives andoutputs the calculated proper IDs. Thus, the proper ID output unit 132may be considered an embodiment of the proper ID output unit 130. Theproper ID output unit 132 operates in the same manner as the proper IDoutput unit 130 in operations other than the above-described operation.

The proper ID output unit 132 has no particular limitation to a methodto calculate proper IDs.

For example, the proper ID output unit 132 may cluster learning images51 into classes and output identifiers (IDs) of the respective classesas calculated proper IDs.

In the case of character images, the proper ID output unit 132 maycalculate gradient direction histograms of the learning images 51,cluster the images based on distances between the histograms, andcalculate proper IDs based on classes that the images belong to.

Alternatively, the proper ID output unit 132 may, without limited to agradient histogram, use another histogram, for example, a line elementhistogram.

In the case of facial images, the proper ID output unit 132 may use aGabor feature in substitution for a histogram.

Further, the proper ID output unit 132 may carry out clustering by usingeither the wholes of the learning images 51 or predetermined regions inthe learning images 51.

As described above, the information processing device 12 calculates theproper IDs based on the learning images 51. Based on the calculatedproper IDs, the information processing device 12 calculates a similaritycalculation method, and registers the calculated similarity calculationmethod in registered patches 200 in a dictionary 20.

The information processing device 12 calculates the proper IDs based onthe learning images 51. In other words, the proper ID is equivalent toclassification of the learning images 51.

Thus, based on the calculated proper IDs, the information processingdevice 12 may select blurring methods used in a blurred image generationunit 120. In other words, the information processing device 12 mayassociate classification of the proper IDs of the learning images 51,which the proper ID output unit 132 calculates, with blurring methodscarried out by the blurred image generation unit 120.

The information processing device 12 according to the second exemplaryembodiment, which is configured in such a manner, is able to achieve anadvantageous effect in reducing operations by a user in addition to theadvantageous effect of the first exemplary embodiment.

The reason for the advantageous effect is as follows.

The reason is that the proper ID output unit 132 of the informationprocessing device 12 calculates the proper IDs based on the learningimages 51. Thus, an operator of the information processing device 12does not have to instruct input of proper IDs or an acquisition positionof the proper IDs.

(Third Exemplary Embodiment)

FIG. 8 is a block diagram illustrating an example of a configuration ofan information processing device 13 according to a third exemplaryembodiment.

In FIG. 8, the same components as in FIG. 1 will be denoted by the samereference numerals and a detailed description thereof will be omitted.

The information processing device 13 may be configured with a computerdevice illustrated in FIG. 6 as with the information processing device10.

The information processing device 13 differs from the first exemplaryembodiment in including an adaptive search similarity calculation unit163 in substitution for the search similarity calculation unit 160.Thus, the adaptive search similarity calculation unit 163 will bedescribed below in detail.

The adaptive search similarity calculation unit 163 calculates asimilarity calculation method, which is to be calculated, in registeredpatches 200 based on not the wholes of learning images 51 butpredetermined local regions thereof. The adaptive search similaritycalculation unit 163 operates in the same manner as the searchsimilarity calculation unit 160 in operations other than theabove-described operation. Thus, the adaptive search similaritycalculation unit 163 is an embodiment of the search similaritycalculation unit 160.

“The predetermined region” in the above description will be describedbelow.

“The predetermined region” in the exemplary embodiment is a region thatcorresponds to a subject (object) included in a learning image 51.

For example, when the learning image 51 is an image of the face of ahuman, the adaptive search similarity calculation unit 163 treats aregion including a portion of the face (the eyes, the nose, the mouth,or the like), as a local region. By using patches in predeterminedregions (for example, regions of the eyes) in the learning images 51,the adaptive search similarity calculation unit 163 calculates asimilarity calculation method that classifies proper IDs appropriately.

Alternatively, when the learning image 51 is an image including numeralsor characters, the adaptive search similarity calculation unit 163 maytreat a character portion or a numeral portion included in apredetermined region as the local region.

The information processing device 13 configured in such a manner is ableto achieve an advantageous effect in that it is possible to register, inthe dictionary 20, the registered patches 200 that make it possible tocreate a super resolution image with a higher accuracy, compared withthe first exemplary embodiment.

In other words, the information processing device 13 is able to achievea higher-accuracy restoration.

The reason for the advantageous effect is as follows.

The information processing device 13 calculates a similarity calculationmethod of the registered patches 200 based on images in predeterminedportions (regions) in the learning images 51, that is, images of similarobjects or closely resembling objects included in the learning images51.

Thus, by using a similarity calculation method by which predeterminedportions can be classified appropriately, the information processingdevice 13 is able to calculate similarities that are suitable for theregions.

In other words, based on a similarity calculation method that issuitable for each portion of the dictionary 20, which the informationprocessing device 13 has created, the super resolution image generationdevice 30 is capable of selecting the registered patches 200. As aresult, the super resolution image generation device 30 is capable ofcompositing (restoring) a super resolution image with a higher accuracy.

As described above, the information processing device 13 is capable ofcreating the dictionary 20 by which the super resolution imagegeneration device 30 is able to restore a blurred image appropriately.

(Fourth Exemplary Embodiment)

There is a case in which a super resolution image is used for imagerecognition.

FIG. 9 is a block diagram illustrating an example of a configuration ofa super resolution system 44 that includes an information processingdevice 14 according to a fourth exemplary embodiment. In FIG. 9, thesame components as in FIG. 1 will be denoted by the same referencenumerals and a detailed description thereof will be omitted.

The super resolution system 44 includes the information processingdevice 14, a dictionary 20, and a super resolution image generationdevice 34. The super resolution system 44 is a system that is suitablefor not only compositing of a super resolution image but also imagerecognition processing.

The information processing device 14 includes a recognition-use featurevector extraction unit 180 in addition to the configuration of theinformation processing device 10 of the first exemplary embodiment. Inrelation to the recognition-use feature vector extraction unit 180, theinformation processing device 14 differs from the information processingdevice 10 in including a proper ID output unit 134 and a registrationunit 174 in substitution for the proper ID output unit 130 and theregistration unit 170.

The super resolution image generation device 34 includes arecognition-use feature vector restoration unit 360 in addition to theconfiguration of the super resolution image generation device 30 of thefirst exemplary embodiment.

Since the other configuration is the same as the first exemplaryembodiment, features that are specific to the exemplary embodiment willbe described.

The information processing device 14 may be configured with a computerdevice that is illustrated in FIG. 6 as with the information processingdevice 10.

The recognition-use feature vector extraction unit 180, based onlearning images 51, extracts recognition-use feature vectors that areused for image recognition processing. The recognition-use featurevector extraction unit 180 transmits the extracted recognition-usefeature vectors to the proper ID output unit 134 and the registrationunit 174.

Based on the recognition-use feature vectors, the proper ID output unit134 calculates proper IDs. In other words, the proper IDs that theinformation processing device 14 calculates are proper IDs that aresuitable for image recognition.

Based on the proper IDs that are suitable for image recognition, thesearch similarity calculation unit 160 calculates a similaritycalculation method.

In other words, the information processing device 14 is capable ofcalculating a similarity calculation method that is suitable for imagerecognition.

The registration unit 174 registers the recognition-use feature vectorsin the dictionary 20 in addition to registration of registered patches200 that include the similarity calculation method.

The recognition-use feature vector restoration unit 360 restores, fromthe dictionary 20, recognition-use feature vectors that are associatedwith registered patches 200 that a selection unit 340 selects. Therecognition-use feature vector restoration unit 360 transmits therestored feature vectors to a not-illustrated image recognition deviceas recognition-use information 57.

The not-illustrated image recognition device carries out imagerecognition that is different from super resolution image processingcarried out by a super resolution image generation device 34. However,the feature vectors that are used for image recognition are featurevectors that are associated with the registered patches 200 that areselected for a super resolution image. Thus, the image recognitiondevice is able to improve accuracy in image recognition of a superresolution image based on the recognition-use information 57.

The proper ID output unit 134 may output proper IDs as with the properID output unit 130 of the first exemplary embodiment.

As described above, the information processing device 14 described inthe fourth exemplary embodiment is able to achieve an advantageouseffect in that it is possible to carry out image recognitionappropriately in addition to the advantageous effects of the firstexemplary embodiment.

The reason for the advantageous effect is as follows.

The information processing device 14 extracts feature vectors that areused for image recognition based on the learning images 51, andregisters the extracted feature vectors in the dictionary 20. Theinformation processing device 14 also calculates a similaritycalculation method based on proper IDs that are suitable for imagerecognition.

The super resolution image generation device 34 is capable oftransmitting feature vectors that are used for image recognition to animage recognition device as recognition-use information 57 in additionto compositing a super resolution image.

Thus, the image recognition device is able to improve accuracy ofrecognition in image recognition of a super resolution image by usingthe feature vectors.

The selection unit 340 of the super resolution image generation device34 is capable of selecting registered patches 200 based on a similaritycalculation method that is suitable for image recognition. In otherwords, a restored image 55 (a super resolution image) that the superresolution image generation device 34 composites is an image that issuitable for image recognition.

Thus, since the image recognition device is able to carry out imagerecognition based on a super resolution image that is suitable for imagerecognition, the image recognition device is able to improve accuracy inimage recognition.

While the invention has been particularly shown and described withreference to exemplary embodiments thereof, the invention is not limitedto these embodiments. It will be understood by those of ordinary skillin the art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the present invention asdefined by the claims.

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2013-079338, filed on Apr. 5, 2013, thedisclosure of which is incorporated herein in its entirety by reference.

The whole or part of the exemplary embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

(Supplementary Note 1)

An information processing device includes:

a proper identifier output unit which outputs proper identifiers foridentifying learning images;

a feature vector calculation unit which calculates feature vectors of atleast a part of patches included in registered patches that areregistered in a dictionary for compositing a restored image; and

a search similarity calculation unit which calculates a similaritycalculation method that classifies the proper identifiers to be given tothe registered patches based on the feature vectors.

(Supplementary Note 2)

The information processing device according to supplementary note 1,further includes:

a learning-use high resolution image reception unit which receives thelearning images;

a blurred image generation unit which generates blurred images of thelearning images;

a patch generation unit which generates patches included in theregistered patches based on the learning images and the blurred images;and

a registration unit which registers the registered patches in thedictionary with including the similarity calculation method.

(Supplementary Note 3)

The information processing device according to supplementary note 2,wherein

the feature vector calculation unit or the patch generation unit givesthe proper identifiers to the registered patches.

(Supplementary Note 4)

The information processing device according to supplementary note 2 or3, wherein

the proper identifier output unit calculates the proper identifiersbased on the learning images.

(Supplementary Note 5)

The information processing device according to any one of supplementarynotes 2 to 4, wherein

the search similarity calculation unit calculates the similaritycalculation method with respect to predetermined regions in the learningimages.

(Supplementary Note 6)

The information processing device according to any one of supplementarynotes 2 to 5, wherein

the blurred image generation unit selects a blurring method to generateblurred images based on the proper identifiers.

(Supplementary Note 7)

The information processing device according to any one of supplementarynotes 1 to 6, further includes:

a recognition-use feature vector extraction unit which extracts featurevectors that are used for image recognition from learning images.

(Supplementary note 8)

An image processing method, includes:

outputting proper identifiers for identifying learning images;

calculating feature vectors of at least a part of patches included inregistered patches that are registered in a dictionary for compositing arestored image; and

calculating a similarity calculation method that classifies the properidentifiers to be given to the registered patches based on the featurevectors.

(Supplementary Note 9)

The image processing method according to supplementary note 8, furtherincludes:

receiving the learning images;

generating blurred images of the learning images;

generating patches included in the registered patches based on thelearning images and the blurred images; and

registering the registered patches in the dictionary with including thesimilarity calculation method.

(Supplementary Note 10)

The image processing method according to supplementary note 8 or 9,further includes:

giving the proper identifiers to the registered patches.

(Supplementary Note 11)

The image processing method according to any one of supplementary notes8 to 10, further includes:

calculating the proper identifiers based on the learning images.

(Supplementary Note 12)

The image processing method according to any one of supplementary notes8 to 11, further includes:

calculating the similarity calculation method with respect topredetermined regions in the learning images.

(Supplementary Note 13)

The image processing method according to any one of supplementary notes8 to 12, further includes:

selecting a blurring method to generate blurred images based on theproper identifiers.

(Supplementary Note 14)

The image processing method according to any one of supplementary notes8 to 13, further includes:

extracting feature vectors that are used for image recognition fromlearning images.

(Supplementary Note 15)

A computer-readable recording medium embodying a program, the programcausing a computer device to perform a method, the method comprising:

outputting proper identifiers for identifying learning images;

calculating feature vectors of at least a part of patches included inregistered patches that are registered in a dictionary for compositing arestored image; and

calculating a similarity calculation method that classifies the properidentifiers to be given to the registered patches based on the featurevectors.

(Supplementary Note 16)

The computer-readable recording medium embodying the program causing thecomputer device to perform the method according to supplementary note15, the method further comprising:

receiving the learning images;

generating blurred images of the learning images;

generating patches included in the registered patches based on thelearning images and the blurred images; and

registering the registered patches in the dictionary with including thesimilarity calculation method.

(Supplementary Note 17)

The computer-readable recording medium embodying the program causing thecomputer device to perform the method according to supplementary note 15or 16, the method further comprising:

giving the proper identifiers to the registered patches.

(Supplementary Note 18)

The computer-readable recording medium embodying the program accordingto the program causing the computer device to perform the methodaccording to supplementary notes 15 to 17, the method furthercomprising:

calculating the proper identifiers based on the learning images.

(Supplementary Note 19)

The computer-readable recording medium embodying the program causing thecomputer device to perform the method according to supplementary notes15 to 18, the method further comprising:

calculating the similarity calculation method with respect topredetermined regions in the learning images.

(Supplementary Note 20)

The computer-readable recording medium embodying the program causing thecomputer device to perform the method according to supplementary notes15 to 19, the method further comprising:

selecting a blurring method to generate blurred images based on theproper identifiers.

(Supplementary Note 21)

The computer-readable recording medium embodying the program causing thecomputer device to perform the method according to supplementary notes15 to 20, the method further comprising:

extracting feature vectors that are used for image recognition fromlearning images.

REFERENCE SINGS LIST

10 Information processing device

11 Information processing device

12 Information processing device

13 Information processing device

14 Information processing device

20 Dictionary

30 Super resolution image generation device

34 Super resolution image generation device

40 Super resolution system

44 Super resolution system

51 Learning image

52 Blurred image

54 Input image

55 Restored image

57 Recognition-use information

60 Information processing device

110 Learning-use high resolution image reception unit

120 Blurred image generation unit

130 Proper ID output unit

132 Proper ID output unit

134 Proper ID output unit

140 Patch generation unit

150 Feature vector calculation unit

160 Search similarity calculation unit

163 Adaptive search similarity calculation unit

170 Registration unit

174 Registration unit

180 Recognition-use feature vector extraction unit

200 Registered patch

310 Low resolution image reception unit

320 Patch generation unit

330 Feature vector calculation unit

340 Selection unit

350 Compositing unit

360 Recognition-use feature vector restoration unit

511 High resolution patch

521 Low resolution patch

531 Patch pair

541 Input patch

551 Restoration patch

610 CPU

620 ROM

630 RAM

640 Internal storage device

650 IOC

660 Input device

670 Display device

680 NIC

700 Storage medium

900 Super resolution system

910 Dictionary creation device

920 Dictionary

930 Super resolution image generation device

The invention claimed is:
 1. An information processing device,comprising: a central processing unit (CPU); and a memory connected tothe CPU; wherein the CPU reads a program from the memory and achievesfunctions of: outputting proper identifiers for identifying learningimages; calculating feature vectors of at least a part of patchesincluded in registered patches that are registered in a dictionary forcompositing a restored image; and calculating a similarity calculationmethod that calculates similarities between patches for classifying theproper identifiers to be given to the registered patches based on thefeature vectors.
 2. The information processing device according to claim1, wherein the CPU reads the program from the memory and furtherachieves functions of: receiving the learning images; generating blurredimages of the learning images; generating patches included in theregistered patches based on the learning images and the blurred images;and registering the registered patches in the dictionary with includingthe similarity calculation method.
 3. The information processing deviceaccording to claim 2, wherein the calculating the feature vectors or thegenerating the patches gives the proper identifiers to the registeredpatches.
 4. The information processing device according to claim 2,wherein the outputting proper identifiers includes calculating theproper identifiers based on the learning images.
 5. The informationprocessing device according to claim 2, wherein the similaritycalculation method is calculated with respect to predetermined regionsin the learning images.
 6. The information processing device accordingto claim 2, wherein the generating the blurred images includes selectinga blurring method to generate blurred images based on the properidentifiers.
 7. The information processing device according to claim 1,wherein the CPU reads the program from the memory and further achievesfunctions of: extracting feature vectors that are used for imagerecognition from learning images.
 8. An image processing method,comprising: outputting proper identifiers for identifying learningimages; calculating feature vectors of at least a part of patchesincluded in registered patches that are registered in a dictionary forcompositing a restored image; and calculating a similarity calculationmethod that calculates similarities between patches for classifying theproper identifiers to be given to the registered patches based on thefeature vectors.
 9. The image processing method according to claim 8,further comprising: receiving the learning images; generating blurredimages of the learning images; generating patches included in theregistered patches based on the learning images and the blurred images;and registering the registered patches in the dictionary with includingthe similarity calculation method.
 10. The image processing methodaccording to claim 8, further comprising: giving the proper identifiersto the registered patches.
 11. The image processing method according toclaim 8, further comprising: calculating the proper identifiers based onthe learning images.
 12. The image processing method according to claim8, further comprising: calculating the similarity calculation methodwith respect to predetermined regions in the learning images.
 13. Theimage processing method according to claim 8, further comprising:selecting a blurring method to generate blurred images based on theproper identifiers.
 14. The image processing method according to claim8, further comprising: extracting feature vectors that are used forimage recognition from learning images.
 15. A non-transitorycomputer-readable recording medium embodying a program, the programcausing a computer device to perform a method, the method comprising:outputting proper identifiers for identifying learning images;calculating feature vectors of at least a part of patches included inregistered patches that are registered in a dictionary for compositing arestored image; and calculating a similarity calculation method thatcalculates similarities between patches for classifying the properidentifiers to be given to the registered patches based on the featurevectors.
 16. The non-transitory computer-readable recording mediumembodying the program causing the computer device to perform the methodaccording to claim 15, the method further comprising: receiving thelearning images; generating blurred images of the learning images;generating patches included in the registered patches based on thelearning images and the blurred images; and registering the registeredpatches in the dictionary with including the similarity calculationmethod.
 17. The non-transitory computer-readable recording mediumembodying the program causing the computer device to perform the methodaccording to claim 15, the method further comprising: giving the properidentifiers to the registered patches.
 18. The non-transitorycomputer-readable recording medium embodying the program causing thecomputer device to perform the method according to claim 15, the methodfurther comprising: calculating the proper identifiers based on thelearning images.
 19. The non-transitory computer-readable recordingmedium embodying the program causing the computer device to perform themethod according to claim 15, the method further comprising: calculatingthe similarity calculation method with respect to predetermined regionsin the learning images.
 20. The non-transitory computer-readablerecording medium embodying the program causing the computer device toperform the method according to claim 15, the method further comprising:selecting a blurring method to generate blurred images based on theproper identifiers.
 21. The non-transitory computer-readable recordingmedium embodying the program causing the computer device to perform themethod according to claim 15, the method further comprising: extractingfeature vectors that are used for image recognition from learningimages.
 22. An information processing device, comprising: properidentifier output means for outputting proper identifiers foridentifying learning images; feature vector calculation means forcalculating feature vectors of at least a part of patches included inregistered patches that are registered in a dictionary for compositing arestored image; and search similarity calculation means for calculatingsimilarities between patches for classifying a similarity calculationmethod that classifies the proper identifiers to be given to theregistered patches based on the feature vectors.